We present a method for improving existing statistical machine translation methods using an informationbase compiled from a bilingual corpus as well as sequence alignment and pattern matching techniques from the area of machine learning and bioinformatics. An alignment algorithm identifies similar sentences, which are then used to construct a better word order for the translation. Our preliminary test results indicate a significant improvement of the translation quality.
Collaborative research, preservation of data, common access to information and recently an increasingly growing amount of data have been an issue in disciplines in which the studied material is located in many places. This is especially the case in the humanities. Digital libraries are a step towards turning hard copies into digitally available material. The need to manage, share, and preserve this information gave birth to a new term, the Digital Humanities. In this paper we present the current state, goals and future outlook of the European Digital Humanities initiative.
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